The Data Science MSc degree consists of 120 credits divided into core courses, elective data science courses, and other courses, as described below. You can find more information about the individual courses by clicking the name of the course. Studies in the Data Science MSc programme include both theoretical and practical components, including a variety of study methods (lectures, exercises, projects, seminars; done both individually and in groups). Especially in applied data science, we also use problem-based learning methods, so that you can address real-world issues. You will also practise academic skills such as scientific writing and oral presentation throughout your studies. You are encouraged to include an internship in your degree in order to obtain practical experience in the field.
Upon graduating from the Data Science MSc programme, you will have solid knowledge of the central concepts, theories, and research methods of data science as well as applied skills. In particular, you will be able to
- Understand the general computational and probabilistic principles underlying modern machine learning and data mining algorithms
- Apply various computational and statistical methods to analyse scientific and business data
- Assess the suitability of each method for the purpose of data collection and use
- Implement state-of-the-art machine learning solutions efficiently using high-performance computing platforms
- Undertake creative work, making systematic use of investigation or experimentation, to discover new knowledge
- Report results in a clear and understandable manner
- Analyse scientific and industrial data to devise new applications and support decision making.
The obligatory core studies in the programme consist of
(a) 20 credits worth of key data science courses:
- Introduction to Data Science
- Introduction to Machine Learning
- Distributed Data Infrastructures
- Statistical Data Science (substitutes Bayesian Inference)
(b) 15 credits worth of courses on professional skills in data science:
You can specialise either in the core areas of data science -- algorithms, infrastructure and statistics -- or in its applications. This means that you can focus on the development of new models and methods in data science, supported by the data science research carried out at the University of Helsinki; or you can become a data science specialist in an application field by incorporating studies in another subject. In addition to mainstream data science topics, the programme offers two largely unique opportunities for specialisation: the data science computing environment and infrastructure, and data science in natural sciences, especially physics.
Minor studies give you a wider perspective of Data Science. Your minor subject can be an application area of Data Science (such as physics or the humanities), a discipline that supports application of Data Science (such as language technology), or a methodological subject needed for the development of new Data Science methods and models (such as computer science, statistics, or mathematics).
You must pick at least four and at most eleven elective courses from the list of data science specialization courses below (20-55 credits). The courses are divided into thematic modules, but courses can be freely taken from any modules. (Other data science courses may be offered as well, but only the ones listed below count towards the required four courses.)
- Advanced Course in Machine Learning
- Probabilistic Graphical Models (not given in 2019-2020)
- Computational Statistics I
Statistical Data Science
- Advanced Bayesian Inference (was Advanced Course in Bayesian Statistics)
- Computational Statistics I
- High Dimensional Statistics
- Spatial Modelling and Bayesian Inference
- Inverse Problems 1: Convolution and Deconvolution
Data Science Infrastructures
- Introduction to Big Data Management
- Cloud and Edge Computing
- Tools of High Performance Computing**
- Big Data Frameworks (not given in 2019-2020)
- Scientific Computing III*
Computers and Cognition
- Introduction to Artificial intelligence
- Philosophy of Artificial Intelligence (not given in 2019-2020)
- Computational Creativity (not given in 2019-2020)
- Interactive Data Visualization
- Cognition & Brain Function (not given in 2019-2020)
- Perception, Communication and Cognition*
Algorithmic Data Science
- Design and Analysis of Algorithms
- Data Compression Techniques**
- String Processing Algorithms*
- Network Analysis
* Given every other academic year (given in 2018-19)
** Given every other academic year (given in 2019-20)
In addition to the above required courses, you can include other courses in your degree as well, for up to 35 credits.
- The Data Science programme offers additional courses and seminars with varying topics.
- The University of Helsinki offers an extensive range of courses across its eleven faculties. The Data Science MSc programme accepts any of these courses.
- Useful method courses are also offered by other programmes, for instance, in computer science, statistics and mathematics, language technology, digital humanities, life science informatics and computational social sciences.
- Studies in an application area of data science can be especially interesting, and can range from ecology to urban studies and from languages to global politics. See a list of all English master's programmes.
- You can also take courses in the international Helsinki Summer School, organised each August.
- An internship in a company or a research group can give you insight into the practice or science of data science. Work experience in data science can also earn you up to 5 credits towards your degree.
In the Master’s thesis, you will demonstrate your familiarity with the thesis topic, mastery of the necessary research methods, the ability to think scientifically and proficiency in academic writing. Your thesis should contain a definition of the research questions, a review of the relevant literature, and theoretical, constructive or empirical parts developing answers to your research questions. The thesis workload, including the collection and processing of the research material as well as the writing process, corresponds approximately to one term of full-time study. More information can be found in the thesis instructions.